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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/16733


    Title: Fuzzy C-Mean Algorithm Based on PSO and Mahalanobis Distances
    Authors: 劉湘川;Liu, Hsiang-Chuan;Yih, Jeng-Ming;Lin, Wen-Chih;Liu, Tung-Sheng
    Contributors: 生物與醫學資訊學系
    Date: 2009-12
    Issue Date: 2012-11-23 17:16:24 (UTC+8)
    Abstract: Some of the well-known fuzzy clustering algorithms are based on Euclidean
    distance function, which can only be used to detect spherical structural clusters. GustafsonKessel
    (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were developed
    to detect non-spherical structural clusters. Both of GG and GK algorithms suffer
    from the singularity problem of covariance matrix and the effect of initial status. In
    this paper, a new Fuzzy C-Means algorithm based on Particle Swarm Optimization and
    Mahalanobis distance without prior information (PSO-FCM-M) is proposed to improve
    those limitations of GG and GK algorithms. And we point out that the PSO-FCM algorithm
    is a special case of PSO-FCM-M algorithm. The experimental results of two real
    data sets show that the performance of our proposed PSO-FCM-M algorithm is better
    than those of the FCM, GG, GK algorithms.
    Relation: International Journal of Innovative Computing Information and Control
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

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